Enterprise Knowledge Fabric.

Problem

Enterprise knowledge lives in a dozen systems, each with its own permission model, freshness, and format. Point-solution RAG against one of them works — until the second, third, and fourth source need the same treatment, each with different authorization rules that a single flat search index can't represent.

Architecture

AQEVON architectural principles

• Source systems remain systems of record — the fabric indexes and governs access, it does not replace them.
• Metadata and ownership are established before indexing, not after.
• Enterprise identity is reused — no parallel permission system.
• Authorization is enforced at retrieval time, per query.
• Hybrid retrieval is preferred over vector-only retrieval for enterprise workloads.
• Generated answers are grounded and cited back to source content.
• Evaluation is continuous — a standing labeled query set, not a one-time launch test.
• Observability separates query, retrieval, and authorization signals into distinct log streams.

Components

Source connectors, a metadata/classification store, a hybrid (lexical + vector) index, an authorization-aware retrieval gateway sitting in front of the LLM/agent layer, and a citation-enforcement step on generation.

Data flow

Source of truth never leaves the origin system. The fabric ingests, classifies, and indexes a governed copy for retrieval — access revocation at the source must propagate to the fabric, not just block future ingestion.

Security

The most common failure mode this architecture is designed against: a system that authenticates the user correctly but retrieves from an index with no document-level authorization, surfacing content the user shouldn't see. Authorization is a retrieval-layer concern here, not an application-layer afterthought.

Trade-offs

Freshness vs. index cost (more frequent re-indexing costs more but reduces staleness); a centralized fabric vs. per-team indexes (centralization governs better but requires more upfront cross-team coordination).

Cost considerations

For many enterprise knowledge workloads, ingestion and re-indexing can become a larger cost driver than query volume — a common budgeting mistake is sizing this like a query-cost problem when it's actually, often, a pipeline-cost problem.

Evaluation

Retrieval precision/recall against a labeled query set, plus a dedicated authorization-leak test — verifying a user genuinely cannot retrieve content outside their entitlements, not just that the UI doesn't display it.

Production considerations

Incremental re-indexing at scale, source-system rate limits, and access-revocation propagation latency (how long after a permission is revoked at the source does the fabric actually stop serving that content?) are the three considerations most often underestimated.

Possible implementation

This is presented as an AQEVON Reference Architecture — a pattern to adapt to a specific environment, not a claimed production customer implementation. Actual implementation details (which vector store, which identity provider integration) are environment-specific decisions made during an architecture engagement.

Lessons learned Illustrative Scenario

The most common risk with this pattern is treating it as a search project instead of an authorization project — teams that get retrieval quality right first and authorization "later" tend to ship a fast, fluent system that leaks data it shouldn't, discovered only once someone asks the wrong question and gets the right-sounding wrong answer.

AQEVON's point of view

RAG is a retrieval technique. Knowledge architecture is a discipline. Most enterprises need the second one and are currently only building the first — this reference architecture is what closes that gap.